Impact of Noise Exposure on Risk of Developing Stress-Related Obstetric Health Effects
Bibliographic record
Abstract
Background: Exposure to noise can increase biological stress reactions and that could increase the risk of stress-related prenatal effects, including adverse obstetric outcomes; however, the association between exposure to noise and adverse obstetric outcomes has not been extensively explored. The objective of this review was to evaluate the evidence between noise exposures and adverse obstetric outcomes, specifically preeclampsia, gestational diabetes, and gestational hypertension. Materials and Methods: A systematic review of English language, comparative studies available in PubMed, Cochrane Central, EMBASE, and CINAHL databases between January 1, 1980 and December 29, 2021 was performed. Risk of bias for individual studies was assessed using the Risk of Bias Instrument for Nonrandomized Studies of Exposures, and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach was used to assess the certainty of the body of evidence for each outcome. Results: Six studies reporting on preeclampsia, gestational diabetes, and gestational hypertension were identified. Although some studies suggested there may be signals of increased responses to increased noise exposure for preeclampsia and gestational hypertension, the certainty in the evidence of an effect of increased noise on all the outcomes was very low due to concerns with risk of bias, inconsistency across studies, and imprecision in the effect estimates. Conclusions: While the certainty of the evidence for noise exposure and adverse obstetric outcomes was very low, the findings from this review may be useful for directing further research in this area, as there is currently limited evidence available. These findings may also be useful for informing guidelines and policies involving noise exposure situations or environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".